Monte Carlo simulation of particle breakage process during grinding

The Monte Carlo method is quite useful in the modeling of particulate systems. It is used here to simulate the particle brekage process during grinding that can be represented by a population balance equation. The simulation technique is free from discretization of time or size. The results of simulation under restricted conditions of grinding compare very well with the available analytical solution of the population balance equation. The procedure is extended to simulate the grinding process in its entirety. This method provides an alternative to the modeling of the grinding process where the governing population balance equation cannot be readily solved.     

Monte Carlo simulation of the diffusion-limited aggregating process of particle suspension systems

The aggregating process of particle suspension systems is a very universal phenomena and crucial for various processes both in nature and in industry. In this paper, the aggregating process was simulated with offlattice diffusion-limited cluster-cluster aggregation (DLCA) Monte Carlo programs. The self-similar fractal structures of aggregates have been clearly demonstrated by the statistical analysis of gyration radius distribution and the existence of a scaling distribution of the reduced cluster size. The fractal dimension determined from the relationship between mass and gyration radius of aggregates was 1.80 or so. The fractal dimension of the aggregates drawn from the radial distribution function and structure factor of a single aggregate is about 1.90–2.10. It was also showed that, along with the increasing of particle volume fraction, the fractal dimension will increase in a nearly square root manner, and the spatial range of the fractal structure appearing becomes narrower. Also, the gelation transition can only occur in a particle suspension system where the particle volume fraction is greater than a critical value.     

Monte Carlo simulation of the diffusion-limited aggregating process of particle suspension systems

The aggregating process of particle suspension systems is a very universal phenomena and crucial for various processes both in nature and in industry. In this paper, the aggregating process was simulated with off-lattice diffusion-limited cluster-cluster aggregation (DLCA) Monte Carlo programs. The self-similar fractal structures of aggregates have been clearly demonstrated by the statistical analysis of gyration radius distribution and the existence of a scaling distribution of the reduced cluster size. The fractal dimension determined from the relationship between mass and gyration radius of aggregates was 1.80 or so. The fractal dimension of the aggregates drawn from the radial distribution function and structure factor of a single aggregate is about 1.90–2.10. It was also showed that, along with the increasing of particle volume fraction, the fractal dimension will increase in a nearly square root manner, and the spatial range of the fractal structure appearing becomes narrower. Also, the gelation transition can only occur in a particle suspension system where the particle volume fraction is greater than a critical value.     

Monte Carlo simulation of terpolymerization

A simulation of terpolymerization, using the Monte Carlo method, and a computer program which performs the calculations for finite and infinite chain length conditions, are described. Terpolymer composition and sequence distributions are given as a function of chain length for two terpolymer systems. It was found that statistical equilibrium is achieved at chain lengths of about 100 units.     

动态Monte Carlo模拟蛋白质与微滤膜相互作用及其对微滤过程影响

构建了二维晶格蛋白质-微滤膜疏水相互作用模型，采用动态Monte Carlo方法模拟了微滤膜污染过程及其受膜孔径、蛋白质浓度和蛋白质结构特性等因素的影响。模拟结果显示：微滤过程中膜通量的变化呈现快速下降、缓慢下降和平台期3个阶段。小孔径微滤膜的滤阻从以膜孔阻力为主转变为以饼层阻力为主;而大孔径微滤膜的滤阻则以膜孔阻力为主。提高蛋白质浓度会强化滤阻从膜孔阻力向饼层阻力的转变。在微滤过程中，蛋白质会因疏水相互作用在膜孔内发生构象转换，进而发生不可逆吸附并形成多层堆积，导致膜污染和通量下降，提高蛋白质的构象稳定性可以显著降低其对微滤膜的污染。分子模拟结果与文献报道的实验结果和理论模型相符，所提供的微观信息对于微滤过程优化和微滤膜设计具有参考作用。     

Monte Carlo simulation of microemulsion polymerization

Monte Carlo simulation of chemically reacting systems based on the master equation was used to describe the stochastic time evolution of the microemulsion polymerization system. A model was developed to demonstrate its applicability for hexyl methacrylate and styrene microemulsion polymerization. The properties of final latex, such as the particle size and molecular weight distributions were obtained simultaneously. The polymerization behavior and properties of final latexes were well reproduced. The model is valuable in confirming or elucidating the various mechanisms in the polymerization. The entry and desorption mechanism was well established to account for the polymerization kinetics. The general polymerization behavior of hydrophobic monomer in microemulsions was properly simulated by the model proposed.     

Monte Carlo simulation of nano-particle sintering

This paper uses a multi-state Potts model to simulate the sintering of nano-particles by boundary migration and evaporation-condensation. The variables in this simulation are the reduced temperature, a next-nearest neighbour weighting and the ratio of interfacial to surface energy. The effect of these parameters on simulation of sintering of two and three-particle clusters is systematically explored as a basis for the study of more complex aggregates.     

Monte Carlo simulation of ferroelectric composites

On the basis of 0–3/1–3 mix‐connectivity model of composites, a new computer model was introduced to simulate the changing of small ceramic particle size of composites. Monte Carlo algorithm was applied to calculate the effects of particle size on dielectric constant, piezoelectric coefficient, pyroelectric coefficient, and other electrical parameters. Compared with other models, this new model was proved to be more reasonable. POLYM. COMPOS., 28:739–742, 2007. © 2007 Society of Plastics Engineers     

Monte Carlo simulation of polyampholyte-nanoparticle complexation

The complexation between a polyampholyte and a charged particle was investigated via Monte Carlo simulations on an off-lattice. The simulations revealed that there are three regions for the conformation of the complex formed between a positively charged particle and a polyampholyte chain. When the charge density and the size of the particle were small, the chain adsorbed on the particle surface maintained to a large extent its configuration from the bulk (spherical, dumb-bell, necklace or rod-like). By increasing the charge density and the particle size, the polyampholyte chain has collapsed on the surface. Further increases of the charge density and size of the particle caused a segregation of the beads of the sub-chains with mostly positive charges from those with mostly negative charges, those richer in positive charges being repelled by the particle. The simulation results are compared with some analytical results.     

Monte Carlo simulation of the PEMFC catalyst layer

The performance of the polymer electrolyte membrane fuel cell (PEMFC) is greatly controlled by the structure of the catalyst layer. Low catalyst utilization is still a significant obstacle to the commercialization of the PEMFC. In order to get a fundamental understanding of the electrode structure and to find the limiting factor in the low catalyst utilization, it is necessary to develop the mechanical model on the effect of catalyst layer structure on the catalyst utilization and the performance of the PEMFC. In this work, the structure of the catalyst layer is studied based on the lattice model with the Monte Carlo simulation. The model can predict the effects of some catalyst layer components, such as Pt/C catalyst, electrolyte and gas pores, on the utilization of the catalyst and the cell performance. The simulation result shows that the aggregation of conduction grains can greatly affect the degree of catalyst utilization. The better the dispersion of the conduction grains, the larger the total effective area of the catalyst is. To achieve higher utilization, catalyst layer components must be distributed by means of engineered design, which can prevent aggregation.     

Monte Carlo simulation of terpolymerization: Optimizing the simulation and post-processing times

Monte Carlo simulations are a useful and easy way to understand a polymerization reaction process properly. However, achieving reliable results with Monte Carlo simulations can also lead to prohibitive computational times and a considerable amount of data to be processed afterward. The present study analyses the Monte Carlo simulation of a steady-state terpolymerization process to reduce the overall computational time of the simulation and the post-processing of its results. Different sorting algorithms (Bubble, Insertion, Selection, and Tim) and Python libraries (Joblib and Numba) were used. The chain composition distribution and the micro-structures resultant of different scenarios were assessed by processing the simulated mechanism results. The simulation time results indicate the Tim sorting algorithm as the best to use in the post-processing step and the Numba library as the best suited for both the simulation and the post-processing step.     

Monte Carlo simulation of particulate matter segregation

Size segregation occurs when particulate matter is subjected to some form of shaking or vibration. This segregation has been the subject of much interest to industries dealing with granular materials. We present the results of new Monte Carlo simulations, which provide insight into the essentially geometric origin of the segregation effect. Results show excellent agreement with segregation observed in experimental systems by other researchers.     

Monte Carlo simulation of microstructure evolution in biphasic-systems

Over the past few decades, a variety of models have been proposed in order to investigate the grain growth kinetics and the development of crystallographic textures in polycrystalline materials. In particular, a full understanding of the microstructure evolution is a key issue for ceramic systems, since their mechanical or thermal behaviour is intimately related to their microstructure. Moreover, the development of appropriate simulative tools is crucial to reproduce, control and finally optimize the solid-state sintering process of ceramics. Monte Carlo simulations are particularly attractive because of their ability to reproduce the statistical behaviour of atoms and grain boundaries with time. However, Monte Carlo simulations applied to two-phase materials, such as many ceramic systems, result complex because both grain growth and diffusion processes should be taken into account. Here the Monte Carlo Potts model, which is widely used to investigate the crystallization kinetics for monophasic systems, is modified and extended to biphasic ones. The proposed model maps the microstructure onto a discrete lattice. Each lattice element contains a number representing its phase and its crystallographic orientation. The grain formation and growth are simulated by appropriate switching and reorientation attempts involving the lattice elements. The effect of temperature is also discussed.     

Monte Carlo simulation of radiation-induced solid state polymerization

A Monte Carlo method was used for a computer simulation of radiation-induced solid state polymerization. The propagation of polymer chains was simulated by means of self-avoiding random walks on a tetrahedral lattice. The initiation and termination of the chains were modelled by pseudorandom processes. The influence of conditions of the in-source process on the post-polymerization kinetics and on the degree of polymerization of the polymers was studied.     

Monte Carlo simulation studies of the catalytic combustion of methane

A Monte Carlo (MC) simulation is carried out of the catalytic oxidation of methane based on the pseudo-Langmuir-Hinshelwood (LH) mechanism proposed by Iglesia et al., that attempts to interpret the behavior of this reaction, which from experiments is assumed to be of the Mars-van Krevelen type. The results interpret reasonably some experimental findings (reaction order with respect to H₂O, CH₄, activity and structural sensitivity) if certain criteria based on the experiment and some laboratory data for the kinetic and thermodynamic parameters from the literature are considered.     

均匀性试验抽样误差的蒙特卡罗模拟

对现行均匀性试验抽样误差和扩大样本容量后的抽样误差,应用蒙特卡罗方法进行数值模拟。结果表明,当总体标准偏差为1.5MPa,样本容量为10,对应水泥28d抗压强度分别为40MPa、50MPa和60MPa时,以变异系数表示的抽样误差分别为1.7%、1.4%和1.1%。这个误差很大,可能导致对水泥均匀性的错误评价。根据扩大样本容量后的抽样误差模拟结果,提出了均匀性试验抽样方法的修改建议。     

Monte Carlo simulation of sequence distributions of acrylonitrile copolymers

A Monte Carlo simulation examining the effect of monomer ratios on the composition and sequence distribution of acrylonitrile(AN) copolymers with N‐vinyl pyrrolidone (NVP), itaconic acid (IA), and acrylic acid (AA) as comonomers has been developed. The Kelen–Tudos method was used to estimate monomer reactivity ratios. The results of the simulation are consistent with the academic conclusion and are as foreseen by the experimental data. The average number of NVP identical monomers in a sequence length of AN/NVP copolymer chain increases continuously and the average number of AN identical monomers in a sequence length shows a prominent decrease with an increase of NVP concentration in the feed. Changes in the monomer average number of AN/IA and AN/AA copolymers in a sequence length were the same as those of AN/NVP copolymer with an increase of comonomer concentration in the feed. The optimum weight ratio of AN with comonomers for manufacturing carbon fibers is 98/2. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 483–488, 2005     

气溶胶在毛细管中输运Monte Carlo模拟

气溶胶在管道中的输运广泛应用于气溶胶采样、监测和气溶胶的危害评价中,前人基于对流扩散方程、流体速度分布规律,结合初始条件和边界条件,对气溶胶粒子在管道中的扩散特性进行了理论分析,取得了一定成功．Monte Carlo方法作为一种统计实验方法,在模拟中子或光子在介质中的Markoff输运过程中取得了巨大成功．另外,Monte Carlo方法也成功用于吸附质在多孔介质中的吸附和解吸特征研究．气溶胶粒子在气体介质中,由于受到气体分子的随机碰撞,